A pristine digital video watermarking in H.264 compressed domain

Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

“…The recommended approach for detecting normal and abnormal instances in thermograms is designed in four key phases. These stages are as follows [10][11][12][13][14][15][16]…”

Section: Methodsmentioning

confidence: 99%

Breast Thermograms Asymmetry Analysis using Gabor filters

Yadlapalli¹,

Reddy²,

Sunitha³

et al. 2021

“…To obtain ground truth depth values and obtain high performance, we employed a CNN deep learning model and a supervised deep learning-based technique. Monocular depth estimation relies on image processing techniques [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and also helpful during communication [29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Monocular Depth Estimation using Transfer learning-An Overview

Swaraja¹,

Akshitha²,

B³

et al. 2021

Self Cite

Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular depth. To begin, we'll go through the various depth estimation techniques and datasets for monocular depth estimation. A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. In addition, multiple deep learning-based monocular depth estimation approaches and models are classified. Finally, the use of transfer learning approaches to monocular depth estimation is illustrated.

“…The approaches outlined above, on the other hand, are particular to the context in which they were trained and so are not domain independent. Monocular depth estimation relies on image processing techniques [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] and is helpful during communication [26][27][28][29][30]. The rest of the work is prearranged as follows.…”

Section: Introductionmentioning

confidence: 99%

CNN Based Monocular Depth Estimation

Swaraja¹,

Pavan²,

Reddy³

et al. 2021

Self Cite

In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates. With the use of transfer learning, this research executes a convolutional neural network for generating a high-resolution depth map from a single RGB image. With a typical encoder-decoder architecture, when initializing the encoder, we use features extracted from high-performing pre-trained networks, as well as augmentation and training procedures that lead to more accurate outcomes. We demonstrate how, even with a very basic decoder, our approach can provide complete high-resolution depth maps. A wide number of deep learning approaches have recently been presented, and they have showed significant promise in dealing with the classical ill-posed issue. The studies are carried out using KITTI and NYU Depth v2, two widely utilized public datasets. We also examine the errors created by various models in order to expose the shortcomings of present approaches which accomplishes viable performance on KITTI besides NYU Depth v2.